--- title: "langchain4j" type: "tool" slug: "langchain4j-langchain4j" canonical_url: "https://www.graphcanon.com/tools/langchain4j-langchain4j" github_url: "https://github.com/langchain4j/langchain4j" homepage_url: "https://docs.langchain4j.dev" stars: 12543 forks: 2358 primary_language: "Java" license: "Apache-2.0" categories: ["llm-frameworks", "developer-tools"] tags: ["inference-serving", "llms", "model-training", "vector-databases", "evaluation-observability", "java", "data-retrieval"] updated_at: "2026-07-07T18:31:53.267189+00:00" --- # langchain4j > LangChain4j is an idiomatic Java library for building LLM-powered applications on the JVM. LangChain4j simplifies integration of large language models (LLMs) and vector stores into Java applications with a unified API. It supports multiple LLM providers and embedding stores, featuring comprehensive tooling for developing chatbots, agents, RAGs, and more, while seamlessly integrating with enterprise frameworks like Quarkus and Spring Boot. ## Facts - Repository: https://github.com/langchain4j/langchain4j - Homepage: https://docs.langchain4j.dev - Stars: 12,543 · Forks: 2,358 · Open issues: 785 · Watchers: 112 - Primary language: Java - License: Apache-2.0 - Last pushed: 2026-07-07T14:44:57+00:00 ## Categories - [LLM Frameworks](/categories/llm-frameworks.md) - [Developer Tools](/categories/developer-tools.md) ## Tags inference & serving, llms, model-training, vector-databases, evaluation & observability, java, data & retrieval ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. 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Here's how: 1. **Unified APIs:** LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Milvus) use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them. To experiment with different LLMs or embedding stores, you can easily switch between them without the need to rewrite your code. LangChain4j currently supports [20+ popular LLM providers](https://docs.langchain4j.dev/integrations/language-models/) and [30+ embedding stores](https://docs.langchain4j.dev/integrations/embedding-stores/). 2. **Comprehensive Toolbox:** Since early 2023, the community has been building numerous LLM-powered applications, identifying common abstractions, patterns, and techniques. LangChain4j has refined these into practical code. Our toolbox includes tools ranging from low-level prompt templating, chat memory management, and function calling to high-level patterns like Agents and RAG. For each abstraction, we provide an interface along with multiple ready-to-use implementations based on common techniques. Whether you're building a chatbot or developing a RAG with a complete pipeline from data ingestion to retrieval, LangChain4j offers a wide variety of options. 3. **Numerous Examples:** These [examples](https://github.com/langchain4j/langchain4j-examples) showcase how to begin creating various LLM-powered applications, providing inspiration and enabling you to start building quickly. LangChain4j began development in early 2023 amid the ChatGPT hype. We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks, and we had to fix that! **Despite the name, LangChain4j is not a Java port of LangChain (Python) — it is built for Java, not ported to it.** It is an idiomatic Java library designed from the ground up around Java conventions: type safety, POJOs, annotations, interfaces, dependency injection, fluent APIs, and first-class integrations with Quarkus, Spring Boot, Helidon, and Micronaut. Its API, internals, and release cycle are independent of the Python LangChain project. We actively monitor community developments, aiming to quickly incorporate new techniques and integrations, ensuring you stay up-to-date. The library is under active development. While some features are still being worked on, the core functionality is in place, allowing you to start building LLM-powered apps now! ## Documentation Documentation can be found [here](https://docs.langchain4j.dev). The documentation chatbot (experimental) can be found [here](https://chat.langchain4j.dev/). ## Getting Started Getting started guide can be found [here](https://docs.langchain4j.dev/get-started). ## Code Examples Please see examples of how LangChain4j can be used in [langchain4j-examples](https://github.com/langchain4j/langchain4j-examples) repo: - [Examples in plain Java](https://github.com/langchain4j/langchain4j-examples/tree/main/other-examples/src/main/java) - [Examples with Quarkus](https://github.com/quarkiverse/quarkus-langchain4j/tree/main/samples) (uses [quarkus-langchain4j](https://github.com/quarkiverse/quarkus-langchain4j) dependency) - [Example with Spring Boot](https://github.com/langchain4j/langchain4j-examples/tree/main/spring-boot-example/src/main/java/dev/langchain4j/example) - [Examples with Helidon](https://github.com/helidon-io/helidon-examples/tree/helidon-4.x/examples/integrations/langchain4j) (uses [io.helidon.integrations.langchain4j](https://mvnrepository.com/artifact/io.helidon.integrations.langchain4j) dependency) - [Examples with Micronaut](https://github.com/micronaut-projects/micronaut-langchain4j/tree/0.3.x/doc-examples/example-op ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/langchain4j-langchain4j`](/api/graphcanon/tools/langchain4j-langchain4j) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_